Reading Group

MIDAS and CSCAR announce a bi-monthly data-science reading group. We intend to build a learning community, have fun, and support each other in our data-science pursuits. The idea for this reading group originated in one of the UM staff data scientist meetings, and the proposed reading group intends to serve and bring together the staff data scientists.

However, this is by no means an exclusive or closed group. Anyone in the UM community is welcome to join and be an equal member of the reading group. In this announcement we lay out the broad goals. We hope that once the group begins to meet, the members will collectively define the scope, and will figure out the best way to learn and support one another.

The main idea is to organize a group that focuses on the foundational ideas and concepts from the contributing disciplines such as applied mathematics, statistics, machine learning, and computer science. But we will also cover readings that lie at the intersection of data science and the larger society and culture. We will preferably read and discuss material that help us develop a sound grip on high-level concepts, have applied flavor, and are not too technical. If you are aware of any material that strips away unnecessary technicalities and reveals the core ideas in an accessible way, please let us know.

Those who wish to pursue more technical details are welcome to meet in smaller sub-groups. Please note that this is not a group focused on coding or implementation of any specific tool. If we do discuss programming, it will be at the more foundational and conceptual level.

What we’re reading

Once Upon an Algorithm: How Stories Explain Computing by Martin Erwig

Once Upon an Algorithm: How Stories Explain Computing by Martin Erwig

Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie Groundhog Day illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter’s world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; “intractable” problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms.

An e-version of the book is available from UM library. You can also check the author’s website at If you prefer, you can buy an audiobook from Amazon, but you will have to supplement your listening with the tables, figures, and equations in the print form.